Methods of and systems for money laundering risk assessment

ABSTRACT

A method of assessing money-laundering risk of an individual includes gathering information regarding the individual. The gathered information includes geographic information and personal information. The method also includes determining a risk value of the geographic information and a risk value of the personal information and calculating a money-laundering risk score using the geographic information risk value and the personal information risk value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is related in subject matter to, andincorporates by reference herein in its entirety, each of the following:

a U.S. patent application Ser. No. 11/548,229 entitled METHODS OF ANDSYSTEMS FOR MONEY-LAUNDERING RISK ASSESSMENT, filed on the same date asthis patent application; and

a U.S. patent application Ser. No. 11/548,235 entitled METHODS OF ANDSYSTEMS FOR MONEY-LAUNDERING RISK ASSESSMENT, filed on the same date asthis patent application.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

BACKGROUND

1. Technical Field

Disclosed embodiments relate generally, by way of example and notlimitation, to systems and methods that permit a risk ofmoney-laundering activity to be assessed.

2. History of Related Art

Money laundering, the metaphorical “cleaning of money” with regard toappearances in law, is the practice of engaging in specific financialtransactions in order to conceal the identity, source, or destination ofmoney. The term “money laundering” has traditionally been applied onlyto financial transactions related to organized crime. However, in recentyears, the definition of money laundering has been expanded bygovernment regulators (e.g., United States Office of the Comptroller ofthe Currency) to encompass any financial transaction that generates anasset or a value as the result of an illegal act. Thus, money launderingis now recognized as potentially practiced by individuals, small andlarge business, corrupt officials, members of organized crime (e.g.,drug dealers or the Mafia) or of cults, and even corrupt states orintelligence agencies.

Anti-money laundering (AML) is a term mainly used in the finance andlegal industries to describe legal controls that require financialinstitutions and other regulated entities to prevent or report moneylaundering activities. For example, financial institutions must performdue diligence by having proof of a customer's identity and that the use,source, and destination of funds do not involve money laundering.

In part due to stringent requirements of the U.S. Patriot Act, which wasenacted after the Sep. 11, 2001 terrorist attacks in an effort to chokethe supply of terror funds, anti-money-laundering efforts have achievedan unprecedented importance on the agendas of U.S. financialinstitutions. In light of the heightened importance to financialinstitutions of impeding money laundering, it would be advantageous toallow financial institutions to more effectively focus resources onthose customers that present a higher risk for money laundering.

SUMMARY

This summary is not intended to represent each embodiment or everyaspect; the following paragraphs of this summary provide representationsof some embodiments as aspects thereof.

A method of assessing money-laundering risk of an individual includesgathering information regarding the individual. The gathered informationincludes geographic information and personal information. The methodalso includes determining a risk value of the geographic information anda risk value of the personal information and calculating amoney-laundering risk score using the geographic information risk valueand the personal information risk value.

A system for assessing money-laundering risk of an individual includes aserver adapted to gather information regarding the individual. Thegathered information includes geographic information and personalinformation. The server is also adapted to determine a risk value of thegeographic information and a risk value of the personal information andcalculate a money-laundering risk score using the geographic informationrisk value and the personal information risk value. The system alsoincludes at least one database interoperably coupled to the server.

An article of manufacture for assessing money-laundering risk of anindividual, the article of manufacture includes at least one computerreadable medium and processor instructions contained on the at least onecomputer readable medium. The processor instructions are configured tobe readable from the at least one computer readable medium by at leastone processor and thereby cause the at least one processor to operate asto gather information regarding the individual. The gathered informationincludes geographic information and personal information. The processorinstructions are also configured to cause the at least one processor tooperate as to determine a risk value of the geographic information and arisk value of the personal information and calculate a money-launderingrisk score using the geographic information risk value and the personalinformation risk value.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of methods and systems may be obtained byreference to the following Detailed Description when taken inconjunction with the accompanying Drawings wherein:

FIG. 1 is a flow diagram of a money-laundering assessment process; and

FIG. 2 is a block diagram of an embodiment of a server.

DETAILED DESCRIPTION

Methods and systems will now be described more fully with reference tothe accompanying drawings in which various embodiment(s) are shown. Themethods and systems may, however, be embodied in many different formsand should not be construed as limited to the embodiments set forthherein; rather, these embodiments are provided so that this disclosurewill be thorough and complete, and will fully convey the scope of thesystems and methods to those skilled in the art.

Various embodiments of the methods and systems set forth herein includea mathematical algorithm that may be used as part of an AML effort. Themathematical algorithm combines specific attributes about afinancial-institution customer, assigns scores and weights to theattributes, and calculates an aggregate customer money-laundering riskscore. The risk score may then be used in an effort to determine anextent of due diligence required for the customer as well as a frequencyof monitoring that may be applied to the customer's account activity forAML purposes.

Various embodiments group customer attributes into three primary riskcategories: 1) people; 2) product; and 3) geography. In a typicalembodiment, the aggregate risk score is created by first assigning anoverall weight to each of the risk categories and adding togetherweighted risk measurements. Table 1 depicts illustrative individualcustomer attributes within each risk category:

TABLE 1 People Product Geography Citizenship Product City OccupationChannel State Select Customer Country Length of relationship Zip CodePhone Number

Within each risk category, the various individual customer attributesmay be assigned risk scores. In various embodiments, the risk scores aremultiplied by customer attribute weights to produce an overall riskcategory score. Certain customer attributes, which are considered keyindicators requiring enhanced due diligence, are assigned trump scores.A trump score assures that a minimum risk score is assigned to a riskcategory whenever a key indicator attribute is present. Trump scores aretypically applied to the customer attributes of citizenship, country,and area code.

Geography Risk Category

Within the Geography risk category, a plurality of attributes can bedefined. In some embodiments, five key attributes are used to indicate acustomer's physical location. Knowing where a customer is physicallylocated is helpful for compliance with AML and Office of Foreign AssetsControl (OFAC) laws and regulations. The OFAC is an office of the UnitedStates Department of the Treasury that administers and enforces economicand trade sanctions based on U.S. foreign policy and national securitygoals against targeted foreign countries, terrorists, internationalnarcotics traffickers, and those engaged in activities related to theunapproved proliferation of weapons of mass destruction. The OFAC actsunder presidential wartime and national emergency powers, as well asauthority granted by specific legislation, to impose controls ontransactions and freeze foreign assets under U.S. jurisdiction. TheSpecially Designated Nationals list provides financial and otherinstitutions with the names of those individuals and organizations thatare currently prohibited from engaging in financial transactions.

In a typical embodiment, government sources, such as lists maintained bythe High Intensity Drug Trafficking Areas (HIDTA) and the OFAC, are usedto identify geographical areas that pose a higher risk for moneylaundering activity. Typical geographic attributes and scoring standardsinclude: 1) city; 2) state; 3) country; 4) zip code; and 5) phonenumber.

City Risk Attribute

In a typical embodiment, city risk is identified using the Office ofNational Drug Control Policy's Profile of Drug Indicator Reports inorder to identify cities within the United States that are major drugdistribution ports, drug transit areas, drug proceeds hubs, or drugtrans-shipment areas. For example, outside of the U.S., foreign citiesnot specified by these sources may be considered a medium risk due tothe lack of money laundering controls and government oversight that mayexist in these locations. In various embodiments, city risk may beassigned as follows: 1) high—U.S. cities that function as major drugdistribution ports, drug transit areas, drug proceed hubs, and drugtrans-shipment areas selected from the High Intensity Drug Traffic Areas(HIDTA) as identified by the Office of National Drug Control Policy; 2)medium—all foreign cities; and 3) low—all other U.S. cities notspecified in the high category.

State Risk Attribute

In a typical embodiment, state risk is identified using the HighIntensity Financial Crime Areas (HIFCA) identified by the FinancialCrimes Enforcement Network (FinCEN). This HIFCA list identifies specificstates and counties that have the highest volume of Bank Secrecy Actfilings and the highest levels of law enforcement responses to moneylaundering concerns within the U.S. Foreign states and provinces notspecified by these sources are identified as medium risk due to the lackof money laundering controls and government oversight that may exist inthese locations. State risk is assigned as follows: 1) high—domesticstates and counties that were identified as HIFCA according to standardsestablished by the FinCEN; 2) medium—all foreign states and provinces;and 3) low—all other states within the U.S. not specified in the highcategory.

Country Risk Attribute

In a typical embodiment, countries are evaluated to determine whichcountries present the greatest money laundering, terrorist financing,and drug trafficking risks. For example, the OFAC list of sanctionedcountries and the Financial Action Task Force (FATF) Non-CooperativeCountries and Territories (NCCT) list may be used to identify high riskcountries. Additionally, countries identified by the HIDTA asinternational drug sources and transit zones may be considered to carryhigh levels of money laundering risk. In various embodiments, all otherforeign countries not specified by one of these sources may beconsidered a medium risk due to the lack of money laundering controlsand government oversight that may exist in these locations. Country riskis assigned as follows: 1) high—countries that are sanctioned by theOFAC and countries and territories on the NCCT designated by the FATFand HIDTA International Drug Sources and Transit Zone Countries; 2)medium—all other foreign countries not specified in the high category;and 3) low—domestic states and U.S. territories not specified in thehigh category.

Zip Code Attribute

In a typical embodiment, zip codes are identified that represent thegeographic areas associated with higher risk cities, counties, andstates assessed in other attribute categories. For example, the datasource for this attribute may be the primary address of the customerand, as a result, may include a U.S. zip code or a foreign postal code.Since city, county, and state boundaries may or may not aligngeographically, zip codes allow the mathematical algorithm to includemore options. Zip code risk is assigned as follows: 1) high—cities,counties, and states recognized by the HIDTA or HIFCA as areas with ahigher risk for money laundering and criminal activity; 2) medium—allother foreign postal codes not specified in the high category; and 3)low—domestic zip codes not specified in the high category.

Phone Number Attribute

In typical embodiment, international telephone country codes areidentified for the countries identified in the country attribute asposing the highest concern for money laundering activity. In particular,the telephone country codes assigned to known OFAC, NCCT, or HIDTAcountries may be considered the highest risk. Telephone country codesfor all other foreign countries may be considered a medium risk due tothe lack of money laundering controls and government oversight that mayexist in these locations. Phone number risk is assigned as follows: 1)high—telephone country codes assigned to OFAC-sanctioned countries,HIDTA international drug sources and transit-zone countries, andcountries on the NCCT list; 2) medium—all other international telephonecountry code numbers not listed in the high category, the U.S. VirginIsles, and Puerto Rico; and 3) low—the U.S. and U.S. territories,excluding Puerto Rico and the U.S. Virgin Isles.

People Risk Category

Within the People risk category, attributes can be identified thatindicate a potentially higher level of risk for money laundering, drugtrafficking, or terrorist financing. In a typical embodiment, themathematical algorithm uses a customer's citizenship, occupation, networth, and length of relationship with a financial institution in aneffort to assess money-laundering risk. The people risk attributes mayinclude citizenship, occupation, net worth, and length of relationshipwith the financial institution.

Citizenship Risk Attribute

In a typical embodiment, the countries of citizenship are evaluated todetermine which present the greatest money laundering, terroristfinancing, and drug trafficking risks. Individuals who are citizens ofOFAC sanctioned countries, FATF NCCT countries, and countries that havebeen identified as having weak anti-money laundering laws or controlsare considered to present the highest risk. All other foreign citizensare typically considered a higher risk due to the lack of moneylaundering controls and government oversight that may exist in theselocations. The citizenship risk scores are assigned as follows: 1)high—citizens of OFAC Sanctioned Countries, NCCT Countries, and HIDTAInternational Drug Source and Transit Zones Countries; 2)medium—citizens of foreign countries other than the OFAC, NCCT, & HIDTAInternational Drug Source and Transit Zone Countries; and 3)low—citizens of the U.S. and U.S. territories.

Occupation Risk Attribute

In a typical embodiment, occupation codes assigned to customers areevaluated to determine which present the greatest money laundering risk.The occupations that are deemed as the highest risk for moneylaundering, drug trafficking, or terrorist financing are grouped intocategories and assigned a risk score of high. Occupations that present ahigher risk for money laundering or tax evasion are designated as amedium risk. Occupation risk is assigned as follows: 1) high—occupationsor characteristics related to high net worth, cash-intensive, publicoffice, legal/accounting/financial, art/jewelry, antiques,import/export, drug-related, military/law enforcement, andweapons/warfare; 2) medium—other cash-intensive occupations that usuallyinvolve smaller dollar amounts; and 3) low—all other occupationselections.

Select Customer Attribute

In various embodiments, a financial institution assigns select status tocustomers who maintain assets at the financial institution above certainthresholds or who conduct substantial levels of business with thefinancial institution across multiple product lines. Select customersare considered a higher risk due to the larger balances usuallyassociated with these accounts. Select customer risk is assigned asfollows: 1) high—all customers designated as Select; 2) medium—NA; and3) low—all customers who do not qualify as a Select customer.

Length of Relationship Attribute

In various embodiments, the mathematical algorithm considers the lengthof relationship that the financial institution has with a customer.Extended customer relationships afford the financial institution theopportunity to perform trend analysis and gain a greater understandingof a customer's transaction behavior. Length of relationship risk isassigned as follows: 1) high—the period of 0-24 months is designated asthe high-risk category since the customer's relationship is new to thefinancial institution and the customer does not have a lengthytransaction history with the financial institution; 2) medium—the periodof 25-120 months is designated as the medium-risk category; and 3)low—the period of 121 months and above is designated as the low-riskcategory because these customers have longstanding relationships andtransaction histories with the financial institution.

In addition to the above, the financial institution may performadditional customer risk analyses, for example, by screening customersagainst lists such as PEP lists and the OFAC Specially DesignatedNationals and Blocked Persons (SDN) List. In typical embodiments,financial institutions may refuse to conduct transactions with, orprovide a product or service to, any persons listed on the OFAC SDNlist.

Product Risk Category

Within the Product risk category, channel and product risks associatedtherewith are assessed.

Product Risk Attribute

In a typical embodiment, risk criteria applied to this assessmentinclude products and services that possess cash value, allowance forcancellation and refunds, accessibility through .com channels, easyconvertibility to cash, transactions in fine jewelry, precious stones ordiamonds, allowance of debit/ATM withdrawals, and fund transfercapabilities. Product risk scores are assigned as follows: 1)high—brokerage, mutual fund, and bank products that have check writingor debit cards, products that allow wire transfers, credit card and billpay products, life insurance products with a cash value and cancellationfeature, precious stone products, and products available for refund; 2)medium—brokerage, mutual fund and bank products that have a cash valuebut no check-writing or debit cards, certificates of deposit, andindividual retirement accounts; and 3) low—all other financial products.

Channel Risk Attribute

In a typical embodiment, within the channel risk category, customersthat use the internet to conduct business present the highestmoney-laundering risk. Although many financial institutions maintainstrong authentication and controls over internet services, the inherentrisks and 24/7 access to these services make the internet services moresusceptible to money laundering abuse than other channels. Channel riskscores are assigned as follows: 1) high—customers who had at least oneinternet session during the preceding three-month period; 2) medium—NA;and 3) low—customers who had no internet sessions in the precedingthree-month period.

Reference Tables

In various embodiments, risk attributes are managed via maintainablereference tables. The mathematical algorithm utilizes the maintainablereference tables as look-up tables in order to assign attribute scoresand risk-category weights. Access to the maintainable reference tablesis restricted to personnel with proper access credentials and changesare documented in a change control log. As a financial institution'sinternal and external environments change, data in the reference tablesmay be adjusted as desired. When the algorithm encounters a null valuefor any risk attribute, a default value is assigned by the algorithm forpurposes of calculating the customer's risk score. In variousembodiments, a customer AML risk assessment process is run periodicallyto take into account changes that may occur to a customer's score overtime.

A risk-assessment mathematical algorithm may be used by financialinstitutions to prioritize customers for investigation for moneylaundering. Those having skill in the art will appreciate that thealgorithm does not replace business judgment nor does it determine whohas engaged in money laundering. Instead, the algorithm attempts toassess which customers are more likely than others to engage in moneylaundering based on a combination of people, geography, and product riskattributes.

Equation (1) is a mathematical-formula that sets forth a mathematicalalgorithm that can be used to perform a customer risk assessment inaccordance with principles of the methods and systems set forth herein.

$\begin{matrix}{{Score} = {\max\left\lbrack {\left( {{W_{Geo}{\sum\limits_{x = {Geo}}^{\;}\;\left( {w_{x}v_{x}} \right)}} + {W_{Peo}{\sum\limits_{x = {Peo}}^{\;}\;\left( {w_{x}v_{x}} \right)}} + {W_{\Pr\mspace{11mu}{od}}{\sum\limits_{x = {\Pr\mspace{11mu}{od}}}^{\;}\;\left( {w_{x}v_{x}} \right)}}} \right),{i_{1}v_{1}},{i_{2}v_{2}},\ldots} \right\rbrack}} & (1)\end{matrix}$In Equation (1), the following variables are used:W_(Cat)=weight ranging between 0.0-1.0 assigned to a particularcategory. (W_(Geo)+W_(Peo)+W_(Prod)=1)w_(x)=weight assigned to attribute x within a given category. (Withineach category, □w_(x)=1.)v_(x)=value assigned to the member for attribute x. (A higher scoredenotes a higher level of risk. For example, assume a member lives inthe U.S. Then v_(Country) for that member may be 1. Similarly, for amember living in a high-risk country, v_(Country) may be 5.)i_(n)=value indicating which attribute(s) should be consideredoverriding on the member's score (i.e., overriding variables) (Forexample, a member living in a high-risk country may have v_(Country)=5.Regardless of the other factors, if i_(Country)=1, the member's scorewould be 5.)i_(n)=0 or 1.

Equation (1) can be used to calculate an average weight for Geography,People, and Product attributes. These averages may then be weightedtogether to come up with an average risk score. A trump function isincluded in Equation (1) so that customers who score high on specificvalues (e.g., country, citizenship, or area code) can be consideredhigher risk, regardless of their average scores. The Score is,therefore, the greater of the average score and the scores received onany of these trump-function values.

Calculation Examples

Assume categories and attributes weighted as follows:

TABLE 2 Geography 0.40 People 0.45 Product 0.15 City 0.15 Citizenship0.50 Products 0.40 State 0.05 Occupation 0.20 Channel 0.60 Country 0.30Select 0.05 Total 1.00 Zip Code 0.25 Relationship 0.25 Phone No. 0.25Total 1.00 Total 1.00

In addition to the above, assume that country, citizenship, and areacode are overriding variables (i.e., i_(Country)=1, i_(Citizenship)=1,i_(AreaCode)=1, i_(AllOther)=0).

Consider the following two customers:

TABLE 3 Customer 1 Customer 2 Attributes Characteristics Value (v_(x))Characteristics Value (v_(x)) City San Antonio 2 San Antonio 2 State TX1 TX 1 Country USA 1 USA 1 Zip Code 78288 1 78288 1 Phone No. 210 1 2101 Citizenship U.S. 1 Colombian 3 Occupation Actuary 3 Actuary 3 SelectNo 1 No 1 Customer Relationship 20 years 1 20 years 1 Length Products 33 3 3 Owned Channel .com 1 .com 1

For this example using Tables 2 and 3, values ranging from 1 (low) to 3(high) are used.

$\begin{matrix}{{{Score}\mspace{14mu}{Customer}\mspace{14mu} 1} = {\max\mspace{14mu}\left\{ \left\lbrack {{.40}\left( {{{.15} \times 2} + {0.5 \times 1} + {{.05} \times 1} + {{.30} \times 1} +} \right.} \right. \right.}} \\{\left. {{025 \times 1} + {{.25} \times 1}} \right) + {{.45}\left( {{{.50} \times 1} + {{.20} \times 3} + {{.05} \times 1} + {{.25} \times 1}} \right)} +} \\\left. {\left. {{.15}\left( {{{.40} \times 3} + {{.60} \times 1}} \right)} \right\rbrack,{1 \times 1},{1 \times 1},{0 \times \ldots}} \right\} \\{= {\max\mspace{14mu}\left\{ {\left\lbrack {{{.40} \times 1.15} + {{.45} \times 1.40} + {{.15} \times 1.80}} \right\rbrack,1,1,0} \right\}}} \\{= {\max\mspace{14mu}\left\{ {1.36,1,1,0} \right\}}} \\{= 1.36}\end{matrix}$ $\begin{matrix}{{{Score}\mspace{14mu}{Customer}\mspace{14mu} 2} = {\max\left\{ \left\lbrack {{.40}\left( {{{.15} \times 2} + {{.05} \times 1} + {{.30} \times 1} + {{.25} \times 1} +} \right.} \right. \right.}} \\{\left. {{.25} \times 1} \right) + {{.45}\left( {{{.50} \times 3} + {{.20} \times 3} + {{.05} \times 1} + {{.25} \times 1}} \right)} +} \\\left. {\left. {{.15}\left( {{{.40} \times 3} + {{.60} \times 1}} \right)} \right\rbrack,{1 \times 1},{1 \times 3},{0 \times \ldots}} \right\} \\{= {\max\mspace{14mu}\left\{ {\left\lbrack {{{.40} \times 1.15} + {{.45} \times 2.40} + {{.15} \times 1.80}} \right\rbrack,1,3,0} \right\}}} \\{= {\max\mspace{14mu}\left\{ {1.81,1,3,0} \right\}}} \\{= 3}\end{matrix}$

It will be apparent to those having skill in the art that the fact thatCustomer 2 is a Colombian citizen results in Customer 2 having a scoreof 3, notwithstanding the fact that the average score resulting fromEquation (1) without taking the trump function into consideration wouldhave been 1.81. In contrast, Customer 1 had no trump function valuesthat were great enough to exceed the average score resulting fromapplication of Equation (1) to the characteristics of Customer 1;therefore, the risk score of Customer 1 remains 1.36.

Referring now to the FIGURES, FIG. 1 is a flow diagram of amoney-laundering assessment process that can employ Equation (1) above.A process flow 100 begins with a customer data extract 102 from acustomer database 105. A single customer database (i.e., the customerdatabase 105) is illustrated within the process flow 100; however, thosehaving skill in the art will appreciate that customer data may beextracted from a variety of sources without departing from principles ofthe methods and systems as set forth herein.

Following the customer data extract 102, the extracted customer data isprovided to a process 104. The process 104 generally depicts theoperations involved in a typical implementation of Equation (1). Inaddition to the input of the customer data extract 102 to the process104, flat files 106 are also input to the process 104. The flat files106 are created from converted reference data 110. The convertedreference data 110 is obtained from a relational database 108. Therelational database 108 houses data regarding the various riskattributes and their assigned values. For example, in the relationaldatabase 108, data may be maintained regarding possible values ofcitizenship, occupation, select customer, length of relationship,product, channel, city, state, country, zip code, and phone number. In atypical embodiment, every U.S. zip code and a risk value associatedtherewith is stored in the relational database 108. The relationaldatabase 108 is, in a typical embodiment, used as a look-up table by theprocess 104.

It will be apparent to those having skill in the art that values for thevarious risk attributes can be readily updated as desired. As notedabove, the process 104 receives as an input the customer data extract102. The customer data extract 102 is illustrated within the process 104as segregated into geographic data 112, people data 114, and productdata 116. Geographic scoring 118 has as inputs the geographic data 112and applicable value data from the flat files 106. In similar fashion,people scoring 112 has as inputs the people data 114 and applicablevalue data of the flat files 106, while product scoring 122 has asinputs the product data 116 and applicable value data of the flat files106. The geographic scoring 118, the people scoring 120, and the productscoring 122 each represent processes by which subscores of overallscores of individuals are calculated.

The geographic scoring 118, the people scoring 120, and the productscoring 122 serve as inputs to geographic data with scores 124, peopledata with scores 126, and product data with scores 128, respectively.The geographic data with scores 124, people data with scores 126, andproduct data with scores 128 combine to create overall scoring 130. Theoverall scoring 130 is calculated using as inputs the geographic datawith scores 124, the people data with scores 126, and the product datawith scores 128. Those having skill in the art will appreciate that,when the overall scoring 130 employs Equation (1), one or more trumpfunctions using an overriding variable may be utilized as dictated bydesign considerations.

The overall scoring 130 is output to detail files 132. The detail files132 are utilized to create extract report data 134. From the extractreport data 134, report detail files 136 are created. From the reportdetail files 136, base reports are formatted, which formatting resultsin reports 140.

FIG. 2 is a block diagram of an embodiment of a server that may be usedto implement various processes as set forth herein. In theimplementation shown, a server 200 may include a bus 218 or othercommunication mechanism for communicating information and a processor202 coupled to the bus 218 for processing information. The server 200also includes a main memory 204, such as a random access memory (RAM) orother dynamic storage device, coupled to the bus 218 for storingcomputer readable instructions to be executed by the processor 202.

The main memory 204 also may be used for storing temporary variables orother intermediate information during execution of the instructions tobe executed by the processor 202. The server 200 further includes a readonly memory (ROM) 206 or other static storage device coupled to the bus218 for storing static information and instructions for the processor202. A computer readable storage device 208, such as a magnetic disk oroptical disk, is coupled to the bus 218 for storing information andinstructions for the processor 202.

The server 200 may be coupled via the bus 218 to a display 210, such asa cathode ray tube (CRT), for displaying information to a user. An inputdevice 212, including, for example, alphanumeric and other keys, iscoupled to the bus 218 for communicating information and commandselections to the processor 202. Another type of user input device is acursor control 214, such as a mouse, a trackball, or cursor directionkeys for communicating direction information and command selections tothe processor 202 and for controlling cursor movement on the display210. The cursor control 214 typically has two degrees of freedom in twoaxes, a first axis (e.g., x) and a second axis (e.g., y), that allow thedevice to specify positions in a plane.

The term “computer readable instructions” as used above refers to anyinstructions that may be performed by the processor 202 and/or othercomponent of the server 200. Similarly, the term “computer readablemedium” refers to any storage medium that may be used to store thecomputer readable instructions. Such a medium may take many forms,including, but not limited to, non volatile media, volatile media, andtransmission media. Non volatile media include, for example, optical ormagnetic disks, such as the storage device 208. Volatile media includedynamic memory, such as the main memory 204. Transmission media includecoaxial cables, copper wire and fiber optics, including wires of the bus218. Transmission can take the form of acoustic or light waves, such asthose generated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer readable media include, forexample, a floppy disk, a flexible disk, hard disk, magnetic tape, anyother magnetic medium, a CD ROM, DVD, any other optical medium, punchcards, paper tape, any other physical medium with patterns of holes, aRAM, a PROM, an EPROM, a FLASH EPROM, any other memory chip orcartridge, a carrier wave, or any other medium from which a computer canread.

Various forms of the computer readable media may be involved in carryingone or more sequences of one or more instructions to the processor 202for execution. For example, the instructions may initially be borne on amagnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to the server 200 canreceive the data on the telephone line and use an infrared transmitterto convert the data to an infrared signal. An infrared detector coupledto the bus 218 can receive the data carried in the infrared signal andplace the data on the bus 218. The bus 218 carries the data to the mainmemory 204, from which the processor 202 retrieves and executes theinstructions. The instructions received by the main memory 204 mayoptionally be stored on the storage device 208 either before or afterexecution by the processor 202.

The server 200 may also include a communication interface 216 coupled tothe bus 218. The communication interface 216 provides a two way datacommunication coupling between the server 200 and, for example, thedatabase 105. For example, the communication interface 216 may be anintegrated services digital network (ISDN) card or a modem used toprovide a data communication connection to a corresponding type oftelephone line. As another example, the communication interface 216 maybe a local area network (LAN) card used to provide a data communicationconnection to a compatible LAN. Wireless links may also be implemented.In any such implementation, the communication interface 216 sends andreceives electrical, electromagnetic, optical, or other signals thatcarry digital data streams representing various types of information.

The storage device 208 can further include instructions for carrying outvarious processes for gathering information about individuals andaccessing reference data for purposes of performing an anti-moneylaundering risk assessment described herein when executed by theprocessor 202. The storage device 208 can further include a database forstoring customer attributes and reference data.

The previous description is of embodiment(s) for implementing themethods and systems described herein, and the scope should not belimited by this description. The scope is instead defined by thefollowing claims.

1. A computer-implemented method of assessing money-laundering risk ofan individual, the method comprising: using a server comprising at leastone processor configured for: gathering information regarding theindividual; wherein the gathered information comprises geographicinformation, personal information, and product information; determininga risk value for each of a plurality of geographic information, thedetermining comprising: determining a risk value for each of a pluralityof geographic information components; weighting each of the plurality ofgeographic-information-component risk values; and summing the pluralityof weighted geographic-information-component risk values; determining bya computer processor a risk value of the personal information, thedetermining comprising: determining a risk value for each of a pluralityof personal information components; weighting each of the plurality ofpersonal-information-component risk values; and summing the plurality ofweighted personal-information-component risk values; determining by acomputer processor a risk value of the product information, thedetermining comprising: determining a risk value for each of a businesschannel type and a product of the individual; weighting each of thebusiness channel type risk value and the product risk value; and summingthe weighted business channel type risk value and the weighted productrisk value; calculating a money-laundering risk score using thegeographic information risk value, the personal information risk value,and the product information risk value; determining whether thegeographic information, personal information, and product informationinclude an overriding risk attribute; wherein overriding risk attributesare a subset including one or more of the plurality of geographic-,personal-, and product-information-components that are particular riskcomponents requiring enhanced consideration, and an individual riskvalue associated therewith; determining a trump score for eachdetermined overriding risk attribute and the individual risk valueassociated therewith; comparing the money-laundering risk score to eachdetermined trump score of each determined overriding risk attribute;replacing the calculated money-laundering risk score with a maximum ofthe respective determined trump scores of each determined overridingrisk attribute when at least one of the respective trump scores exceedsthe calculated money-laundering risk score; and using a resultingselection of the calculated money-laundering risk score or the maximumof the respective determined trump scores to assess the money-launderingrisk of the individual.
 2. The method of claim 1, wherein the geographicinformation comprises at least one of city, state, country, postal code,and telephone number.
 3. The method of claim 1, wherein the personalinformation comprises at least one of citizenship, occupation, statuswith a financial institution, and length of a relationship with thefinancial institution.
 4. The method of claim 1, wherein the step ofcalculating the money-laundering risk score using the geographicinformation risk value and the personal information risk valuecomprises: weighting the geographic information risk value; weightingthe personal information risk value; and summing the weighted geographicinformation risk value and the weighted personal information risk value.5. The method of claim 1, further comprising flagging the individual forheightened scrutiny responsive to the calculated money-laundering riskscore exceeding a threshold.
 6. A system of assessing money-launderingrisk of an individual, the system comprising: a server comprising atleast one processor configured to: gather information regarding theindividual; wherein the gathered information comprises geographicinformation, personal information, and product information; determine arisk value of the geographic information, the determining comprising:determining a risk value for each of a plurality of personal informationcomponents; weighting each of the plurality ofgeographic-information-component risk values; and summing the pluralityof weighted geographic-information-component risk values; determine arisk value of the personal information, the determining comprising:determining a risk value for each of a plurality of personal informationcomponents; weighting each of the plurality ofpersonal-information-component risk values; and summing the plurality ofweighted personal-information-component risk values; and determine arisk value of the product information, the determining comprising:determining a risk value for each of a business channel type and aproduct of the individual; weighting each of the business channel typerisk value and the product risk value; and summing the weighted businesschannel type risk value and the weighted product risk value; calculate amoney-laundering risk score using the geographic information risk value,the personal information risk value, and the product information riskvalue; determine whether the geographic information, personalinformation, and product information include an overriding riskattribute, wherein overriding risk attributes are a subset including oneor more of the plurality of geographic-, personal-, andproduct-information-components that are particular risk componentsrequiring enhanced consideration, and an individual risk valueassociated therewith; determine a trump score for each determinedoverriding risk attribute and the individual risk value associatedtherewith; compare the money-laundering risk score to each determinedtrump score of each determined overriding risk attribute; replace thecalculated money-laundering risk score with a maximum of the respectivedetermined trump scores of each determined overriding risk attributewhen at least one of the respective trump scores exceeds the calculatedmoney-laundering risk score; and use a resulting selection of thecalculated money-laundering risk score or the maximum of the respectivedetermined trump scores to assess the money-laundering risk of theindividual.
 7. The system of claim 6, wherein the geographic informationcomprises at least one of city, state, country, postal code, andtelephone number.
 8. The system of claim 6, wherein the personalinformation comprises at least one of citizenship, occupation, statuswith a financial institution, and length of a relationship with thefinancial institution.
 9. The system of claim 6, wherein the calculationof the money-laundering risk score using the geographic information riskvalue and the personal information risk value comprises: weighting thegeographic information risk value; weighting the personal informationrisk value; and summing the weighted geographic information risk valueand the weighted personal information risk value.
 10. The system ofclaim 6, wherein the server is adapted to flag the individual forheightened scrutiny responsive to the calculated money-laundering riskscore exceeding to a threshold.
 11. A computer-readable storage mediumencoded with computer-readable instructions for assessingmoney-laundering risk of an individual, the computer-readableinstructions comprising instructions for causing a computer to: use aserver comprising at least one processor configured to: gatherinformation regarding the individual; wherein the gathered informationcomprises geographic information, personal information, and productinformation; determine a risk value of the geographic information, thedetermining comprising: determining a risk value for each of a pluralityof personal information components; weighting each of the plurality ofgeographic-information-component risk values; and summing the pluralityof weighted geographic-information-component risk values; determine arisk value of the personal information, the determining comprising:determining a risk value for each of a plurality of personal informationcomponents; weighting each of the plurality ofpersonal-information-component risk values; and summing the plurality ofweighted personal-information-component risk values; and determine arisk value of the product information, the determining comprising:determining a risk value for each of a business channel type and aproduct of the individual; weighting each of the business channel typerisk value and the product risk value; and summing the weighted businesschannel type risk value and the weighted product risk value; calculate amoney-laundering risk score using the geographic information risk value,the personal information risk value, and the product information riskvalue; determine whether the geographic information, personalinformation, and product information include an overriding riskattribute wherein overriding risk attributes are a subset including oneor more of the plurality of geographic-, personal-, andproduct-information-components that are particular risk componentsrequiring enhanced consideration, and an individual risk valueassociated therewith; determine a trump score for each determinedoverriding risk attribute and the individual risk value associatedtherewith; compare the money-laundering risk score to each determinedtrump score of each determined overriding risk attribute; replace thecalculated money-laundering risk score with a maximum of the respectivedetermined trump scores of each determined overriding risk attributewhen at least one of the respective trump scores exceeds the calculatedmoney-laundering risk score; and use a resulting selection of thecalculated money-laundering risk score or the maximum of the respectivedetermined trump scores to assess the money-laundering risk of theindividual.
 12. The computer-readable storage medium of claim 11,wherein the geographic information comprises at least one of city,state, country, postal code, and telephone number.
 13. Thecomputer-readable storage medium of claim 11, wherein the personalinformation comprises at least one of citizenship, occupation, statuswith a financial institution, and length of a relationship with thefinancial institution.
 14. The computer-readable storage medium of claim11, wherein the calculation of the money-laundering risk score using thegeographic information risk value and the personal information riskvalue comprises: weighting the geographic information risk value;weighting the personal information risk value; and summing the weightedgeographic information risk value and the weighted personal informationrisk value.
 15. The computer-readable storage medium of claim 11,wherein the instructions cause the computer to operate as a flag to theindividual for heightened scrutiny responsive to the calculatedmoney-laundering risk score exceeding a threshold.